In strategic settings a player may be able to influence the behavior of an opponent by revealing information about their own characteristics. They may for example aim to exploit stereotypes held by others. We provide an experimental test of this. A substantial fraction of players in a trust game exhibit a positive willingness to pay to reveal a photograph of themselves to their randomly-assigned partner. This suggests that they perceive that they can use their own characteristics to influence the behavior of others. The demand for such self-revelation depends negatively on price.

Mayshar et al. (2022) apply an instrumental variables identification strategy to data from nearly 1,000 societies included in the Ethnographic Atlas to claim that cultivation of cereals (appropriable by elites), rather than increased land productivity following the adoption of agriculture, led to the development of the state. We show two things. (1) Evidence for the appropriability theory holds when moving from a tribe-chiefdom to a state and not more broadly. (2) Conclusions are driven by a handful of outliers with statistical significance at the 10% level lost with winsorization at 2.1% (or trimming at 1.2%) of locations by cereal advantage.

In Brodeur, Cook, and Heyes (2020) we present evidence that instrumental variable (and to a lesser extent difference-in-difference) articles are more p-hacked than randomized controlled trial and regression discontinuity design articles. We also find no evidence that (i) articles published in the top five journals are different; (ii) the “revise and resubmit” process mitigates the problem; (iii) things are improving through time. Kranz and Pütz (2022) apply a novel adjustment to address rounding errors. They successfully replicate our results with the exception of our shakiest finding: after adjusting for rounding errors, bunching of test statistics for difference-in-difference articles is now smaller around the 5 percent level (and coincidentally larger at the 10 percent level). (JEL A14, C12, C52)

The credibility revolution in economics has promoted causal identification using randomized control trials (RCT), difference-in-differences (DID), instrumental variables (IV) and regression discontinuity design (RDD). Applying multiple approaches to over 21,000 hypothesis tests published in 25 leading economics journals, we find that the extent of p-hacking and publication bias varies greatly by method. IV (and to a lesser extent DID) are particularly problematic. We find no evidence that (i) papers published in the Top 5 journals are different to others; (ii) the journal “revise and resubmit” process mitigates the problem; (iii) things are improving through time. (JEL A14, C12, C52)